# Runaway argument? [closed]

Suddenly my document stopped producing pdf and I can't seem to find out what's causing this error:

Runaway argument? \let \AtEndDocument \@firstofone \@enddocumenthook \@checkend {docume\ETC. ! File ended while scanning use of \beamer@collect@@body. \par <*> example.tex

?
Process interrupted by user

This is what I get afterwards:

And deleting those files didn't fix the issue:

What should I do? I'm not sure if the post should include my code since it's long and I don't know which part is causing error to paste it here.

Here's the MWE:

\documentclass{beamer}
\usepackage{graphicx}

\usetheme{Warsaw}
\title[Artificial Neural Network]{Introduction  to ANN\\Artificial Neural Network}
\author{Gigili}
\date{January 6, 2013}
\begin{document}

\begin{frame}
\titlepage{}

\end{frame}

\begin{frame}
We are drowning in information and starving for knowledge.
\hspace*{70mm}      – Rutherford D. Roger

\end{frame}

\begin{frame}{definitions of ANN}

\centerline{\includegraphics[scale=0.6]{anatomy}}

end{frame}

\begin{frame}{Important factors to consider choosing an ANN architecture}

- the number and type of neuronal layers and the number of neurons comprising each layer

- Creating the NN architecture therefore means coming up with values for the number of
layers of each type and the number of nodes in each of these layers.

Following this schema this will give you a competent architecture but probably not an
optimal one.

But once this network is initialized, you can iteratively tune the configuration
during training using a number of ancillary algorithms; one family of these works
by pruning nodes based on (small) values of the weight vector after a certain number
of training epochs--in other words, eliminating unnecessary/redundant nodes.

\end{frame}

\begin{frame}{main types of neural network architecture}

- Feed Forward such as MLP: capable of approximating continuous and integrable
functions, consists of one input and one output layer and one or more hidden layers.

The activities of the neurons in each layer are a non-linear function of the activities
in the layer below.

- Recurrent: These have directed cycles in their connection graph.
That means you can sometimes get back to where you started by following the arrows.

- Symmetrically connected networks: These are like recurrent networks,
but the connections between units are symmetrical
(they have the same weight in both directions).

\end{frame}

\begin{frame}

\centerline{\includegraphics[scale=0.6]{MLP}}

\end{frame}

\begin{frame}

\centerline{\includegraphics[scale=0.6]{recurrent}}
\begin{center}Recurrent structure \end{center}

\end{frame}

\begin{frame}{classfication using NN- continue}

Possible solutions:

1) Trial and error (experience)\\
2) adds and delets neurons as needed during training \\
3) constructive algorithms, network pruning, regularization

\end{frame}
\end{document}

-
Creating a minimal working example is actually the best thing you can do (either to help yourself or to encourage others to help you). – jon Jan 8 at 4:57
Save your document as another file then strip the new file until you can reproduce your error with a minimal code. In most cases, you can pinpoint the error by doing this. If this doesn't solve your problem, then you can post the MWE here. – hpesoj626 Jan 8 at 4:58
Line 28 in my editor: You have a missing backslash in end{frame}. – hpesoj626 Jan 8 at 5:10
@hpesoj626: Right! That happened while I was creating the MWE. – Gigili Jan 8 at 5:17
@Gigili Did that solve your problem though? Sans figures, your code compiles fine in my machine if I put backslash in end{frame}. If it did, then I am voting to close this question since it is too localized. :) – hpesoj626 Jan 8 at 5:20